Exploration of the chemical space of DNA‐encoded libraries

2022 ◽  
Author(s):  
Regina PIKALYOVA ◽  
Yuliana Zabolotna ◽  
Dmitriy Volochnyuk ◽  
Dragos Horvath ◽  
Marcous Gilles ◽  
...  
Keyword(s):  
Chemical Space

Total Synthesis of Axially-Chiral Cannabinols: A New Platform for Cannabinoid-Based Drug Discovery

2019 ◽  
Author(s):  
Primali Navaratne ◽  
Jenny Wilkerson ◽  
Kavindri Ranasinghe ◽  
Evgeniya Semenova ◽  
Lance McMahon ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Total Synthesis ◽  
Ground State ◽  
Chemical Space ◽  
Modern Medicine ◽  
The Novel ◽  
Pharmaceutical Development ◽  
Bioactive Component ◽  
Axially Chiral ◽  
New Directions

<div> <div> <div> <p>Phytocannabinoids, molecules isolated from cannabis, are gaining attention as promising leads in modern medicine, including pain management. Considering the urgent need for combating the opioid crisis, new directions for the design of cannabinoid-inspired analgesics are of immediate interest. In this regard, we have hypothesized that axially-chiral-cannabinols (ax-CBNs), unnatural (and unknown) isomers of cannabinol (CBN) may be valuable scaffolds for cannabinoid-inspired drug discovery. There are multiple reasons for thinking this: (a) ax-CBNs would have ground-state three-dimensionality akin to THC, a key bioactive component of cannabis, (b) ax-CBNs at their core structure are biaryl molecules, generally attractive platforms for pharmaceutical development due to their ease of functionalization and stability, and (c) atropisomerism with respect to phytocannabinoids is unexplored “chemical space.” Herein we report a scalable total synthesis of ax-CBNs, examine physical properties experimentally and computationally, and provide preliminary behavioral and analgesic analysis of the novel scaffolds. </p> </div> </div> </div>

Metal-, and Organocatalyst-Free One-Pot Assembly of Chiral Aza-Tricyclic Molecules: Creating Six Contiguous Stereocenters from 2-D-Structures and an Amino Acid

2020 ◽  
Author(s):  
Dung Do
Keyword(s):  
Amino Acid ◽  
Chemical Space ◽  
Structural Diversity ◽  
Therapeutic Agents ◽  
Chiral Molecules ◽  
One Pot ◽  
Complex Molecules ◽  
Chiral Catalyst ◽  
Chiral Complex ◽  
Free Process

<p>Chiral molecules with their defined 3-D structures are of paramount importance for the study of chemical biology and drug discovery. Having rich structural diversity and unique stereoisomerism, chiral molecules offer a large chemical space that can be explored for the design of new therapeutic agents.<sup>1</sup> Practically, chiral architectures are usually prepared from organometallic and organocatalytic processes where a transition metal or an organocatalyst is tailor-made for desired reactions. As a result, developing a method that enables rapid assembly of chiral complex molecules under metal- and organocatalyst-free condition represents a daunting challenge. Here we developed a straightforward route to create a chiral 3-D structure from 2-D structures and an amino acid without any chiral catalyst. The center of this research is the design of a <a>special chiral spiroimidazolidinone cyclohexadienone intermediate</a>, a merger of a chiral reactive substrate with multiple nucleophillic/electrophillic sites and a transient organocatalyst. <a>This unique substrate-catalyst (“subcatalyst”) dual role of the intermediate enhances </a><a>the coordinational proximity of the chiral substrate and catalyst</a> in the key Aza-Michael/Michael cascade resulting in a substantial steric discrimination and an excellent overall diastereoselectivity. Whereas the “subcatalyst” (hidden catalyst) is not present in the reaction’s initial components, which renders a chiral catalyst-free process, it is strategically produced to promote sequential self-catalyzed reactions. The success of this methodology will pave the way for many efficient preparations of chiral complex molecules and aid for the quest to create next generation of therapeutic agents.</p>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

General Cyclopropane Assembly via Enantioselective Redox-Active Carbene Transfer to Aliphatic Olefins

2018 ◽  
Author(s):  
Marc Montesinos-Magraner ◽  
Matteo Costantini ◽  
Rodrigo Ramirez-Contreras ◽  
Michael E. Muratore ◽  
Magnus J. Johansson ◽  
...  
Keyword(s):  
Total Synthesis ◽  
Asymmetric Synthesis ◽  
Chemical Space ◽  
Synthetic Approach ◽  
Asymmetric Cyclopropanation ◽  
Redox Active ◽  
Wide Range ◽  
Leaving Group ◽  
Stereoelectronic Properties ◽  
Carbene Transfer

Asymmetric cyclopropane synthesis currently requires bespoke strategies, methods, substrates and reagents, even when targeting similar compounds. This limits the speed and chemical space available for discovery campaigns. Here we introduce a practical and versatile diazocompound, and we demonstrate its performance in the first unified asymmetric synthesis of functionalized cyclopropanes. We found that the redox-active leaving group in this reagent enhances the reactivity and selectivity of geminal carbene transfer. This effect enabled the asymmetric cyclopropanation of a wide range of olefins including unactivated aliphatic alkenes, enabling the 3-step total synthesis of (–)-dictyopterene A. This unified synthetic approach delivers high enantioselectivities that are independent of the stereoelectronic properties of the functional groups transferred. Our results demonstrate that orthogonally-differentiated diazocompounds are viable and advantageous equivalents of single-carbon chirons<i>.</i>

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

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